DCVC-MV: Deep Contextual Multiview Video Compression with Efficient Inter-View Prediction

Abstract

Multiview video is a key format for 3D applications such as free-viewpoint broadcasting and virtual reality, yet its large data volume poses significant challenges for efficient storage and transmission. As deep contextual video compression matures and moves toward standardization, extending such learned codecs to multiview scenarios has become essential for practical deployment---yet this direction remains largely unexplored. In this paper, we propose DCVC-MV, a novel deep contextual multiview video compression framework that satisfies three fundamental requirements. First, it maintains backward compatibility, ensuring that the primary view's bitstream can be decoded independently by a single-view decoder without being affected by other views. Second, it supports random-access capability, enabling flexible switching between different viewing perspectives. Third, it effectively exploits inter-view correlations to achieve high compression efficiency. This is realized through four dedicated components: (1) an inter-view motion feature propagation method, which propagates decoded independent-view motion features as conditions to promote dependent-view motion encoding; (2) an inter-view motion conditional entropy model designed to learn motion conditional priors across views for more accurate probability estimation of motion latent representations; (3) an implicit inter-view context prediction method, which predicts inter-view contexts from low-resolution independent-view content features without explicit disparity estimation; and (4) an inter-view contextual conditional entropy model that learns contextual conditional priors across views to further enhance content compression.